Medical Image Hierarchical Multi-Label Classification (MI-HMC) is of
paramount importance in modern healthcare, presenting two significant
challenges: data imbalance and \textit{hierarchy constraint}. Existing
solutions involve complex model architecture design or domain-specific
preprocessing, demanding considerable expertise or effort in implementation. To
address these limitations, this paper proposes Transfer Learning with Maximum
Constraint Module (TLMCM) network for the MI-HMC task. The TLMCM network offers
a novel approach to overcome the aforementioned challenges, outperforming
existing methods based on the Area Under the Average Precision and Recall
Curve($AU\overline{(PRC)}$) metric. In addition, this research proposes two
novel accuracy metrics, $EMR$ and $HammingAccuracy$, which have not been
extensively explored in the context of the MI-HMC task. Experimental results
demonstrate that the TLMCM network achieves high multi-label prediction
accuracy($80\%$-$90\%$) for MI-HMC tasks, making it a valuable contribution to
healthcare domain applications.

本论文提出了一种用于医学图像层次多标签分类的转移学习与最大约束模块网络，通过解决数据不平衡和层次约束等问题，提高了多标签预测准确度，在医疗应用领域具有重要价值。

医学图像分层多标签分类的 TLMCM 网络

TLMCM Network for Medical Image Hierarchical Multi-Label Classification

Hierarchical multi-label classification (HMC) is a challenging classification
task extending standard multi-label classification problems by imposing a
hierarchy constraint on the classes. In this paper, we propose C-HMCNN(h), a
novel approach for HMC problems, which, given a network h for the underlying
multi-label classification problem, exploits the hierarchy information in order
to produce predictions coherent with the constraint and improve performance. We
conduct an extensive experimental analysis showing the superior performance of
C-HMCNN(h) when compared to state-of-the-art models.

本文提出了一种新的层次化多标签分类问题的方法 C-HMCNN (h)，该方法利用网络 h 中的层次信息，以产生符合约束的预测并提高性能，在广泛的实验分析中展示了与最先进模型相比的优异性能。

相干的层次多标签分类网络

Coherent Hierarchical Multi-Label Classification Networks

In many learning settings, it is beneficial to augment the main features with
pairwise interactions. Such interaction models can be often enhanced by
performing variable selection under the so-called strong hierarchy constraint:
an interaction is non-zero only if its associated main features are non-zero.
Existing convex optimization based algorithms face difficulties in handling
problems where the number of main features $p \sim 10^3$ (with total number of
features $\sim p^2$). In this paper, we study a convex relaxation which
enforces strong hierarchy and develop a highly scalable algorithm based on
proximal gradient descent. We introduce novel screening rules that allow for
solving the complicated proximal problem in parallel. In addition, we introduce
a specialized active-set strategy with gradient screening for avoiding costly
gradient computations. The framework can handle problems having dense design
matrices, with $p = 50,000$ ($\sim 10^9$ interactions)---instances that are
much larger than current state of the art. Experiments on real and synthetic
data suggest that our toolkit hierScale outperforms the state of the art in
terms of prediction and variable selection and can achieve over a 4900x
speed-up.

提出了一种基于凸松弛和近端梯度下降的高度可扩展算法，该算法采用了新的筛选规则和专门的激活集策略，能够处理具有密集设计矩阵的问题，并在预测和变量选择方面优于当前技术水平。